{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "maml.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mari-linhares/tensorflow-maml/blob/master/maml.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "UNDl_8BVB2Zw",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# MAML reimplementation using TensorFlow 2.0\n",
        "\n",
        "Reproduction of MAML using TensorFlow 2.0.\n",
        "\n",
        "This  is highly influenced by the pytorch reproduction by Adrien Lucas Effot: [Paper repro: Deep Metalearning using “MAML” and “Reptile”](https://towardsdatascience.com/paper-repro-deep-metalearning-using-maml-and-reptile-fd1df1cc81b0)\n",
        "\n",
        "Github: [https://github.com/mari-linhares/tensorflow-maml](https://github.com/mari-linhares/tensorflow-maml)\n",
        "\n",
        "Twitter: [@hereismari](https://twitter.com/hereismari)\n",
        "\n",
        "## MAML paper\n",
        "\n",
        "https://arxiv.org/abs/1703.03400\n",
        "\n",
        "**Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks**\n",
        "*Chelsea Finn, Pieter Abbeel, Sergey Levine*\n",
        "\n",
        "> We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning. The goal of meta-learning is to train a model on a variety of learning tasks, such that it can solve new learning tasks using only a small number of training samples. In our approach, the parameters of the model are explicitly trained such that a small number of gradient steps with a small amount of training data from a new task will produce good generalization performance on that task. In effect, our method trains the model to be easy to fine-tune. We demonstrate that this approach leads to state-of-the-art performance on two few-shot image classification benchmarks, produces good results on few-shot regression, and accelerates fine-tuning for policy gradient reinforcement learning with neural network policies.\n",
        "\n",
        "---\n",
        "\n",
        "![image.png](https://cdn-images-1.medium.com/max/1600/1*EUt0H5AOEFkERg-OzfCC7A.png)\n"
      ]
    },
    {
      "metadata": {
        "id": "FWTeNOF2B2Zy",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### TensorFlow installation"
      ]
    },
    {
      "metadata": {
        "id": "13iE3EYmB2Zz",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!pip install -q tensorflow-gpu==2.0.0-alpha0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "umzZEZF2B2Z2",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Imports"
      ]
    },
    {
      "metadata": {
        "id": "QMyjiTD0B2Z3",
        "colab_type": "code",
        "outputId": "e8abef65-fc80-4766-8005-9ff05e266d07",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        }
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow.keras as keras\n",
        "import tensorflow.keras.backend as keras_backend\n",
        "\n",
        "\n",
        "# Other dependencies\n",
        "import random\n",
        "import sys\n",
        "import time\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Reproduction\n",
        "np.random.seed(333)\n",
        "\n",
        "\n",
        "print('Python version: ', sys.version)\n",
        "print('TensorFlow version: ', tf.__version__)\n",
        "\n",
        "device_name = tf.test.gpu_device_name()\n",
        "if device_name != '/device:GPU:0':\n",
        "  raise SystemError('GPU device not found')\n",
        "print('GPU found at: {}'.format(device_name))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Python version:  3.6.7 (default, Oct 22 2018, 11:32:17) \n",
            "[GCC 8.2.0]\n",
            "TensorFlow version:  2.0.0-alpha0\n",
            "GPU found at: /device:GPU:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "frb0xYyPB2Z6",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Section 5.1 Evaluating MAML on regression\n",
        "\n",
        "> *We start with a simple regression problem that illustrates\n",
        "the basic principles of MAML. Each task involves regressing\n",
        "from the input to the output of a sine wave, where the\n",
        "amplitude and phase of the sinusoid are varied between\n",
        "tasks. Thus, p(T ) is continuous, where the amplitude\n",
        "varies within [0.1, 5.0] and the phase varies within [0, π],\n",
        "and the input and output both have a dimensionality of 1.\n",
        "During training and testing, datapoints x are sampled uniformly\n",
        "from [−5.0, 5.0].*\n",
        "\n",
        "![]()"
      ]
    },
    {
      "metadata": {
        "id": "xqC7EfTNB2Z7",
        "colab_type": "code",
        "outputId": "7ac39c7d-31bb-4635-bf38-f07f3263eb6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "cell_type": "code",
      "source": [
        "class SinusoidGenerator():\n",
        "    '''\n",
        "        Sinusoid Generator.\n",
        "        \n",
        "        p(T) is continuous, where the amplitude varies within [0.1, 5.0]\n",
        "        and the phase varies within [0, π].\n",
        "        \n",
        "        This abstraction is the basically the same defined at:\n",
        "        https://towardsdatascience.com/paper-repro-deep-metalearning-using-maml-and-reptile-fd1df1cc81b0  \n",
        "    '''\n",
        "    def __init__(self, K=10, amplitude=None, phase=None):\n",
        "        '''\n",
        "        Args:\n",
        "            K: batch size. Number of values sampled at every batch.\n",
        "            amplitude: Sine wave amplitude. If None is uniformly sampled from\n",
        "                the [0.1, 5.0] interval.\n",
        "            pahse: Sine wave phase. If None is uniformly sampled from the [0, π]\n",
        "                interval.\n",
        "        '''\n",
        "        self.K = K\n",
        "        self.amplitude = amplitude if amplitude else np.random.uniform(0.1, 5.0)\n",
        "        self.phase = phase if amplitude else np.random.uniform(0, np.pi)\n",
        "        self.sampled_points = None\n",
        "        self.x = self._sample_x()\n",
        "        \n",
        "    def _sample_x(self):\n",
        "        return np.random.uniform(-5, 5, self.K)\n",
        "    \n",
        "    def f(self, x):\n",
        "        '''Sinewave function.'''\n",
        "        return self.amplitude * np.sin(x - self.phase)\n",
        "\n",
        "    def batch(self, x = None, force_new=False):\n",
        "        '''Returns a batch of size K.\n",
        "        \n",
        "        It also changes the sape of `x` to add a batch dimension to it.\n",
        "        \n",
        "        Args:\n",
        "            x: Batch data, if given `y` is generated based on this data.\n",
        "                Usually it is None. If None `self.x` is used.\n",
        "            force_new: Instead of using `x` argument the batch data is\n",
        "                uniformly sampled.\n",
        "        \n",
        "        '''\n",
        "        if x is None:\n",
        "            if force_new:\n",
        "                x = self._sample_x()\n",
        "            else:\n",
        "                x = self.x\n",
        "        y = self.f(x)\n",
        "        return x[:, None], y[:, None]\n",
        "    \n",
        "    def equally_spaced_samples(self, K=None):\n",
        "        '''Returns `K` equally spaced samples.'''\n",
        "        if K is None:\n",
        "            K = self.K\n",
        "        return self.batch(x=np.linspace(-5, 5, K))\n",
        "        \n",
        "        \n",
        "def plot(data, *args, **kwargs):\n",
        "    '''Plot helper.'''\n",
        "    x, y = data\n",
        "    return plt.plot(x, y, *args, **kwargs)\n",
        "\n",
        "\n",
        "for _ in range(3):\n",
        "    plt.title('Sinusoid examples')\n",
        "    plot(SinusoidGenerator(K=100).equally_spaced_samples())\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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FZwl0NtIvs07TOPYdmFtDH22EIBNyEziVe4oXhrxglPGi0/J5d2MC08uGMK9y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\nURQb8//dNT4xtB8RxAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "J937ypttB2aA",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Generate sinusoid datasets\n"
      ]
    },
    {
      "metadata": {
        "id": "mFF-mv-mB2aB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def generate_dataset(K, train_size=20000, test_size=10):\n",
        "    '''Generate train and test dataset.\n",
        "    \n",
        "    A dataset is composed of SinusoidGenerators that are able to provide\n",
        "    a batch (`K`) elements at a time.\n",
        "    '''\n",
        "    def _generate_dataset(size):\n",
        "        return [SinusoidGenerator(K=K) for _ in range(size)]\n",
        "    return _generate_dataset(train_size), _generate_dataset(test_size) \n",
        "\n",
        "train_ds, test_ds = generate_dataset(K=10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hU01nb4nB2aD",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Training a regular Neural Network\n",
        "\n",
        "We're training a Neural Network for given a batch of size *K* of randomly sampled values (*x*) predict *f(x)* where f is a sine wave function with amplitude randomly sampled within [0.1, 5.0] and the phase randomly sampled within [0, π]."
      ]
    },
    {
      "metadata": {
        "id": "10EEnZ8dB2aE",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Model definition\n",
        "\n",
        "\n",
        "> *The regressor\n",
        "is a neural network model with 2 hidden layers of size\n",
        "40 with ReLU nonlinearities.*"
      ]
    },
    {
      "metadata": {
        "id": "jevbblhFB2aF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class SineModel(keras.Model):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.hidden1 = keras.layers.Dense(40, input_shape=(1,))\n",
        "        self.hidden2 = keras.layers.Dense(40)\n",
        "        self.out = keras.layers.Dense(1)\n",
        "        \n",
        "    def forward(self, x):\n",
        "        x = keras.activations.relu(self.hidden1(x))\n",
        "        x = keras.activations.relu(self.hidden2(x))\n",
        "        x = self.out(x)\n",
        "        return x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "j86O3DOcB2aH",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Training implementation"
      ]
    },
    {
      "metadata": {
        "id": "9_OOYo7NB2aI",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def loss_function(pred_y, y):\n",
        "  return keras_backend.mean(keras.losses.mean_squared_error(y, pred_y))\n",
        "\n",
        "def np_to_tensor(list_of_numpy_objs):\n",
        "    return (tf.convert_to_tensor(obj) for obj in list_of_numpy_objs)\n",
        "    \n",
        "\n",
        "def compute_loss(model, x, y, loss_fn=loss_function):\n",
        "    logits = model.forward(x)\n",
        "    mse = loss_fn(y, logits)\n",
        "    return mse, logits\n",
        "\n",
        "\n",
        "def compute_gradients(model, x, y, loss_fn=loss_function):\n",
        "    with tf.GradientTape() as tape:\n",
        "        loss, _ = compute_loss(model, x, y, loss_fn)\n",
        "    return tape.gradient(loss, model.trainable_variables), loss\n",
        "\n",
        "\n",
        "def apply_gradients(optimizer, gradients, variables):\n",
        "    optimizer.apply_gradients(zip(gradients, variables))\n",
        "\n",
        "    \n",
        "def train_batch(x, y, model, optimizer):\n",
        "    tensor_x, tensor_y = np_to_tensor((x, y))\n",
        "    gradients, loss = compute_gradients(model, tensor_x, tensor_y)\n",
        "    apply_gradients(optimizer, gradients, model.trainable_variables)\n",
        "    return loss\n",
        "\n",
        "\n",
        "def train_model(dataset, epochs=1, lr=0.001, log_steps=1000):\n",
        "    model = SineModel()\n",
        "    optimizer = keras.optimizers.Adam(learning_rate=lr)\n",
        "    for epoch in range(epochs):\n",
        "        losses = []\n",
        "        total_loss = 0\n",
        "        start = time.time()\n",
        "        for i, sinusoid_generator in enumerate(dataset):\n",
        "            x, y = sinusoid_generator.batch()\n",
        "            loss = train_batch(x, y, model, optimizer)\n",
        "            total_loss += loss\n",
        "            curr_loss = total_loss / (i + 1.0)\n",
        "            losses.append(curr_loss)\n",
        "            \n",
        "            if i % log_steps == 0 and i > 0:\n",
        "                print('Step {}: loss = {}, Time to run {} steps = {:.2f} seconds'.format(\n",
        "                    i, curr_loss, log_steps, time.time() - start))\n",
        "                start = time.time()\n",
        "        plt.plot(losses)\n",
        "        plt.title('Loss Vs Time steps')\n",
        "        plt.show()\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "07d5-wOdB2aL",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Train Model"
      ]
    },
    {
      "metadata": {
        "id": "EOtx-6JSB2aL",
        "colab_type": "code",
        "outputId": "90904f1c-5f34-4a3f-bfdf-90993458d1b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        }
      },
      "cell_type": "code",
      "source": [
        "neural_net = train_model(train_ds)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Step 1000: loss = 3.60124964472607, Time to run 1000 steps = 9.27 seconds\n",
            "Step 2000: loss = 3.577827891295407, Time to run 1000 steps = 8.43 seconds\n",
            "Step 3000: loss = 3.4642712184469002, Time to run 1000 steps = 8.42 seconds\n",
            "Step 4000: loss = 3.384379187868702, Time to run 1000 steps = 8.37 seconds\n",
            "Step 5000: loss = 3.350723381465459, Time to run 1000 steps = 8.49 seconds\n",
            "Step 6000: loss = 3.3131153435936533, Time to run 1000 steps = 9.89 seconds\n",
            "Step 7000: loss = 3.2739911694952735, Time to run 1000 steps = 9.07 seconds\n",
            "Step 8000: loss = 3.2469566893702635, Time to run 1000 steps = 9.73 seconds\n",
            "Step 9000: loss = 3.2144259681777076, Time to run 1000 steps = 8.42 seconds\n",
            "Step 10000: loss = 3.196497376083734, Time to run 1000 steps = 8.39 seconds\n",
            "Step 11000: loss = 3.191781134430839, Time to run 1000 steps = 8.36 seconds\n",
            "Step 12000: loss = 3.1824173898416377, Time to run 1000 steps = 8.39 seconds\n",
            "Step 13000: loss = 3.1788955732241853, Time to run 1000 steps = 8.39 seconds\n",
            "Step 14000: loss = 3.1661123096854955, Time to run 1000 steps = 8.36 seconds\n",
            "Step 15000: loss = 3.17040673644126, Time to run 1000 steps = 8.41 seconds\n",
            "Step 16000: loss = 3.16726201373556, Time to run 1000 steps = 8.35 seconds\n",
            "Step 17000: loss = 3.170990698549754, Time to run 1000 steps = 9.78 seconds\n",
            "Step 18000: loss = 3.158513459003026, Time to run 1000 steps = 8.87 seconds\n",
            "Step 19000: loss = 3.1582199989616644, Time to run 1000 steps = 8.35 seconds\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kUEmlmRmnL5zLmisWYwbZvPOea+/j0Rd37tbuf93y6Oj0iZffPTq98v+eRTqhUxFFpPIU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\niPn/5lIzyeMMQgoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "q5P2yS3GB2aP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "A neural network trained for this task, should converge to the average of the sinusoids curves."
      ]
    },
    {
      "metadata": {
        "id": "5kDFTIVlB2aQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def plot_model_comparison_to_average(model, ds, model_name='neural network', K=10):\n",
        "    '''Compare model to average.\n",
        "    \n",
        "    Computes mean of training sine waves actual `y` and compare to\n",
        "    the model's prediction to a new sine wave, the intuition is that\n",
        "    these two plots should be similar.\n",
        "    '''\n",
        "    sinu_generator = SinusoidGenerator(K=K)\n",
        "    \n",
        "    # calculate average prediction\n",
        "    avg_pred = []\n",
        "    for i, sinusoid_generator in enumerate(ds):\n",
        "        x, y = sinusoid_generator.equally_spaced_samples()\n",
        "        avg_pred.append(y)\n",
        "    \n",
        "    x, _ = sinu_generator.equally_spaced_samples()    \n",
        "    avg_plot, = plt.plot(x, np.mean(avg_pred, axis=0), '--')\n",
        "\n",
        "    # calculate model prediction\n",
        "    model_pred = model.forward(tf.convert_to_tensor(x))\n",
        "    model_plot, = plt.plot(x, model_pred.numpy())\n",
        "    \n",
        "    # plot\n",
        "    plt.legend([avg_plot, model_plot], ['Average', model_name])\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "NTwdhHxpB2aR",
        "colab_type": "code",
        "outputId": "9f3a32ce-70d6-4a60-dce8-15d7e9a20df2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "cell_type": "code",
      "source": [
        "plot_model_comparison_to_average(neural_net, train_ds)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HETwYeo6E/e9SfKmS/1h1jMPn7GsYqC0SQBIQIYQIF0I4A4uANdeeIIS4pnMK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\nUEopl9IEoJRSLqUJQCmlXOp/oihveC/gdd4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "oO1ZsJyfB2aT",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Transfer learning?\n",
        "\n",
        "Here we try to use the trained neural network to initialize the weights for a model trained on new sine wave functions.\n",
        "\n",
        "The new sine wave is treated as a new task. Our model was trained on a bunch of sine wave tasks and we're trying to use this previous knowledge to initialize the weights of the model for a new task."
      ]
    },
    {
      "metadata": {
        "id": "7_fOqvY0B2aU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def copy_model(model, x):\n",
        "    '''Copy model weights to a new model.\n",
        "    \n",
        "    Args:\n",
        "        model: model to be copied.\n",
        "        x: An input example. This is used to run\n",
        "            a forward pass in order to add the weights of the graph\n",
        "            as variables.\n",
        "    Returns:\n",
        "        A copy of the model.\n",
        "    '''\n",
        "    copied_model = SineModel()\n",
        "    \n",
        "    # If we don't run this step the weights are not \"initialized\"\n",
        "    # and the gradients will not be computed.\n",
        "    copied_model.forward(tf.convert_to_tensor(x))\n",
        "    \n",
        "    copied_model.set_weights(model.get_weights())\n",
        "    return copied_model\n",
        "\n",
        "\n",
        "def eval_sine_test(model, optimizer, x, y, x_test, y_test, num_steps=(0, 1, 10)):\n",
        "    '''Evaluate how the model fits to the curve training for `fits` steps.\n",
        "    \n",
        "    Args:\n",
        "        model: Model evaluated.\n",
        "        optimizer: Optimizer to be for training.\n",
        "        x: Data used for training.\n",
        "        y: Targets used for training.\n",
        "        x_test: Data used for evaluation.\n",
        "        y_test: Targets used for evaluation.\n",
        "        num_steps: Number of steps to log.\n",
        "    '''\n",
        "    fit_res = []\n",
        "    \n",
        "    tensor_x_test, tensor_y_test = np_to_tensor((x_test, y_test))\n",
        "    \n",
        "    # If 0 in fits we log the loss before any training\n",
        "    if 0 in num_steps:\n",
        "        loss, logits = compute_loss(model, tensor_x_test, tensor_y_test)\n",
        "        fit_res.append((0, logits, loss))\n",
        "        \n",
        "    for step in range(1, np.max(num_steps) + 1):\n",
        "        train_batch(x, y, model, optimizer)\n",
        "        loss, logits = compute_loss(model, tensor_x_test, tensor_y_test)\n",
        "        if step in num_steps:\n",
        "            fit_res.append(\n",
        "                (\n",
        "                    step, \n",
        "                    logits,\n",
        "                    loss\n",
        "                )\n",
        "            )\n",
        "    return fit_res\n",
        "\n",
        "\n",
        "def eval_sinewave_for_test(model, sinusoid_generator=None, num_steps=(0, 1, 10), lr=0.01, plot=True):\n",
        "    '''Evaluates how the sinewave addapts at dataset.\n",
        "    \n",
        "    The idea is to use the pretrained model as a weight initializer and\n",
        "    try to fit the model on this new dataset.\n",
        "    \n",
        "    Args:\n",
        "        model: Already trained model.\n",
        "        sinusoid_generator: A sinusoidGenerator instance.\n",
        "        num_steps: Number of training steps to be logged.\n",
        "        lr: Learning rate used for training on the test data.\n",
        "        plot: If plot is True than it plots how the curves are fitted along\n",
        "            `num_steps`.\n",
        "    \n",
        "    Returns:\n",
        "        The fit results. A list containing the loss, logits and step. For\n",
        "        every step at `num_steps`.\n",
        "    '''\n",
        "    \n",
        "    if sinusoid_generator is None:\n",
        "        sinusoid_generator = SinusoidGenerator(K=10)\n",
        "        \n",
        "    # generate equally spaced samples for ploting\n",
        "    x_test, y_test = sinusoid_generator.equally_spaced_samples(100)\n",
        "    \n",
        "    # batch used for training\n",
        "    x, y = sinusoid_generator.batch()\n",
        "    \n",
        "    # copy model so we can use the same model multiple times\n",
        "    copied_model = copy_model(model, x)\n",
        "    \n",
        "    # use SGD for this part of training as described in the paper\n",
        "    optimizer = keras.optimizers.SGD(learning_rate=lr)\n",
        "    \n",
        "    # run training and log fit results\n",
        "    fit_res = eval_sine_test(copied_model, optimizer, x, y, x_test, y_test, num_steps)\n",
        "    \n",
        "    # plot\n",
        "    train, = plt.plot(x, y, '^')\n",
        "    ground_truth, = plt.plot(x_test, y_test)\n",
        "    plots = [train, ground_truth]\n",
        "    legend = ['Training Points', 'True Function']\n",
        "    for n, res, loss in fit_res:\n",
        "        cur, = plt.plot(x_test, res[:, 0], '--')\n",
        "        plots.append(cur)\n",
        "        legend.append(f'After {n} Steps')\n",
        "    plt.legend(plots, legend)\n",
        "    plt.ylim(-5, 5)\n",
        "    plt.xlim(-6, 6)\n",
        "    if plot:\n",
        "        plt.show()\n",
        "    \n",
        "    return fit_res"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1DfDKk92B2aW",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Try to use transfer learning for different sine waves"
      ]
    },
    {
      "metadata": {
        "id": "Af3x__XaB2aW",
        "colab_type": "code",
        "outputId": "0918fd8b-0068-45ff-e905-5ef44968f756",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 773
        }
      },
      "cell_type": "code",
      "source": [
        "for index in np.random.randint(0, len(test_ds), size=3):\n",
        "    eval_sinewave_for_test(neural_net, test_ds[index])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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028yxX68RdzWNbmPl2i8ASStXcX3I0AJxv0tcVhwh8SGM3T621ItWkoUFkkqF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\nKBQKO0MFdoVCobAzVGBXKBQKO+P/AZdALqc9fvkjAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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028yxX68RdzWNbmPl2i8ASStXcX3I0AJxv0tcVhwh8SGM3T621ItWkoUFkkqF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\nKBQKO0MFdoVCobAzVGBXKBQKO+P/AZdALqc9fvkjAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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028yxX68RdzWNbmPl2i8ASStXcX3I0AJxv0tcVhwh8SGM3T621ItWkoUFkkqF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\nKBQKO0MFdoVCobAzVGBXKBQKO+P/AZdALqc9fvkjAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "oUVjGk8YB2aY",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### MAML\n",
        "\n",
        "![](https://cdn-images-1.medium.com/max/1600/1*_pgbRGIlmCRsYNBHl71mUA.png)"
      ]
    },
    {
      "metadata": {
        "id": "xzVi0_YfB2aZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def train_maml(model, epochs, dataset, lr_inner=0.01, batch_size=1, log_steps=1000):\n",
        "    '''Train using the MAML setup.\n",
        "    \n",
        "    The comments in this function that start with:\n",
        "        \n",
        "        Step X:\n",
        "        \n",
        "    Refer to a step described in the Algorithm 1 of the paper.\n",
        "    \n",
        "    Args:\n",
        "        model: A model.\n",
        "        epochs: Number of epochs used for training.\n",
        "        dataset: A dataset used for training.\n",
        "        lr_inner: Inner learning rate (alpha in Algorithm 1). Default value is 0.01.\n",
        "        batch_size: Batch size. Default value is 1. The paper does not specify\n",
        "            which value they use.\n",
        "        log_steps: At every `log_steps` a log message is printed.\n",
        "    \n",
        "    Returns:\n",
        "        A strong, fully-developed and trained maml.\n",
        "    '''\n",
        "    optimizer = keras.optimizers.Adam()\n",
        "    \n",
        "    # Step 2: instead of checking for convergence, we train for a number\n",
        "    # of epochs\n",
        "    for _ in range(epochs):\n",
        "        total_loss = 0\n",
        "        losses = []\n",
        "        start = time.time()\n",
        "        # Step 3 and 4\n",
        "        for i, t in enumerate(random.sample(dataset, len(dataset))):\n",
        "            x, y = np_to_tensor(t.batch())\n",
        "            model.forward(x)  # run forward pass to initialize weights\n",
        "            with tf.GradientTape() as test_tape:\n",
        "                # test_tape.watch(model.trainable_variables)\n",
        "                # Step 5\n",
        "                with tf.GradientTape() as train_tape:\n",
        "                    train_loss, _ = compute_loss(model, x, y)\n",
        "                # Step 6\n",
        "                gradients = train_tape.gradient(train_loss, model.trainable_variables)\n",
        "                k = 0\n",
        "                model_copy = copy_model(model, x)\n",
        "                for j in range(len(model_copy.layers)):\n",
        "                    model_copy.layers[j].kernel = tf.subtract(model.layers[j].kernel,\n",
        "                                tf.multiply(lr_inner, gradients[k]))\n",
        "                    model_copy.layers[j].bias = tf.subtract(model.layers[j].bias,\n",
        "                                tf.multiply(lr_inner, gradients[k+1]))\n",
        "                    k += 2\n",
        "                # Step 8\n",
        "                test_loss, logits = compute_loss(model_copy, x, y)\n",
        "            # Step 8\n",
        "            gradients = test_tape.gradient(test_loss, model.trainable_variables)\n",
        "            optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
        "            \n",
        "            # Logs\n",
        "            total_loss += test_loss\n",
        "            loss = total_loss / (i+1.0)\n",
        "            losses.append(loss)\n",
        "            \n",
        "            if i % log_steps == 0 and i > 0:\n",
        "                print('Step {}: loss = {}, Time to run {} steps = {}'.format(i, loss, log_steps, time.time() - start))\n",
        "                start = time.time()\n",
        "        plt.plot(losses)\n",
        "        plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "b8IbVFxhJgI5",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Training MAML\n",
        "\n",
        "It should take around 40 seconds to train for 1000 steps."
      ]
    },
    {
      "metadata": {
        "id": "Yt9ZS-XXB2aa",
        "colab_type": "code",
        "outputId": "02c1c19c-0ffb-4c16-b33f-f375cf926d40",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 615
        }
      },
      "cell_type": "code",
      "source": [
        "maml = SineModel()\n",
        "train_maml(maml, 1, train_ds)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Step 1000: loss = 2.835608691010521, Time to run 1000 steps = 37.25773525238037\n",
            "Step 2000: loss = 2.2980676687281822, Time to run 1000 steps = 38.47769522666931\n",
            "Step 3000: loss = 1.9418990904599958, Time to run 1000 steps = 36.885146379470825\n",
            "Step 4000: loss = 1.6723755686877, Time to run 1000 steps = 38.40376925468445\n",
            "Step 5000: loss = 1.498474668932346, Time to run 1000 steps = 39.0564181804657\n",
            "Step 6000: loss = 1.3577355284714336, Time to run 1000 steps = 38.49018621444702\n",
            "Step 7000: loss = 1.2540965596851796, Time to run 1000 steps = 36.95879244804382\n",
            "Step 8000: loss = 1.1666453083357842, Time to run 1000 steps = 39.42040467262268\n",
            "Step 9000: loss = 1.0938589063051456, Time to run 1000 steps = 36.88026213645935\n",
            "Step 10000: loss = 1.032974551922229, Time to run 1000 steps = 38.38460731506348\n",
            "Step 11000: loss = 0.9790446101984127, Time to run 1000 steps = 36.82742977142334\n",
            "Step 12000: loss = 0.9363318125547105, Time to run 1000 steps = 38.35729169845581\n",
            "Step 13000: loss = 0.8993095653221509, Time to run 1000 steps = 37.980260372161865\n",
            "Step 14000: loss = 0.8643644600710318, Time to run 1000 steps = 38.67064452171326\n",
            "Step 15000: loss = 0.8351736643296398, Time to run 1000 steps = 37.38232207298279\n",
            "Step 16000: loss = 0.8068722559634701, Time to run 1000 steps = 37.864771366119385\n",
            "Step 17000: loss = 0.7819695129136875, Time to run 1000 steps = 38.47490167617798\n",
            "Step 18000: loss = 0.7607096916484757, Time to run 1000 steps = 36.80701684951782\n",
            "Step 19000: loss = 0.7394962343473276, Time to run 1000 steps = 38.36602592468262\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "6bCe8vojB2ad",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Use MAML model to fit new sine wave"
      ]
    },
    {
      "metadata": {
        "id": "t8uD3D7wB2ae",
        "colab_type": "code",
        "outputId": "50e7596f-fbeb-4a6f-db76-c8c9f4abb77b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 773
        }
      },
      "cell_type": "code",
      "source": [
        "for index in np.random.randint(0, len(test_ds), size=3):\n",
        "    eval_sinewave_for_test(maml, test_ds[index])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PnHRHJndLJPYzwF2KorRQFMUGeBz4wwLntbiorCiWBy5HCEFr19Z0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\nCyGEjfn/PZc6DjwL8LQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PnHRHJndLJPYzwF2KorRQFMUGeBz4wwLntbiorCiWBy5HCEFr19Z0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\nCyGEjfn/PZc6DjwL8LQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sGrCIKM8o/r3r31wuvVyj9d2MtnNnQtasxr5ZMB0GNiM/rZSfK0bRRpFMiJzC\n59vO2TQ+4e8cYmPJbjsUZ097/MPcLBfjV4Jshrb38euFXxm+bjj7Lu3j2ZhnWXvPWvo07VPten20\nPrzZ9U3WD1/PXc3uYsXZFYzZMIZSQ2m1y24oRFdMLTKYDCw+uZj58fMpM5bhYe/BhMgJaNVaYnxj\nKFy3jgtbjqEO8aJF97Ba2eLU1c6VD3t9yP5L+/HR+tR4fTdyddaDqbAQZ+NFihUyupJYDHYK7pZ2\nMTsuiKf7hYm+9jpCe8+9ZO/ZRYcBfkiKKz+rx5eBf3vwDqeFWkmPgB68GPsiTZyaWL3+pi5NebfH\nu0zrMI347PjK/vcZe2bQuUlnBjQbgEJqnG1XMd2xliQVJPHcH89xvvA8dwbdyX0t76ODbwdUij9/\ntybfNw7TpQs06ZWOdtp28Ait9TjrwkrV1H89Sc6+g8zs+Qat9XZ0df+YlqpE+ho/Z2zHYNHXXgcU\nbdmCU7dulJvUSApwcNIgZyawZMldnA7pyjsjVtgkrsKKQh785UEuFF6glUcrXoh9gU5NGs7hHlWd\n7tg4f53ZgKeDJ1q1lrn95jKr7yw6N+n8t6RefuoUpceO4x5hQBsUbpOkvuH8BiZumojRbNuDLjwn\nTcK+tIiglN9Z6FzBz3I0gVIObcxnOXwx/+YFCDWq/MwZ0p96moLVa9C6aHBw0mAym/i/na/wgac7\nBVo3KkwVNonN1c6Vtfes5YOeH1BQUcCkzZN46renyNY1rjMgRGKvYdm6bIxmI652riwZvIRegb2u\neV/eD8uIi32FRMcYaHNvLUdpYae0Iy4zjuVnltuk/qu0Me1x7N2LyWk7OfdqDz6YPp1iAljdI42N\nz/S0aWwCFK5eTZF7c7ZfCqcgU4fepOfFHS+yojSJSbgx+6552ClttxOpQlIwOHQw64ev55mYZ0gu\nTK7sprF1o6W2iMReg4r0RUzcNJHXdr8GcMMujmydAyWO/rhqsm22IKd/cH+6B3Tno4MfseWibU8x\n8pk2DXNRETkLFrLyP6f5w/AKnFwDJoNN42rsZL2ewnXryYkZSVaqDq2Lhue3P8+Wi1t4ITefZ9s+\nbvOuvKvsVfZMbjOZtfesRavWYjAbuHfDvXxw4ANyy3JtHV6NEom9hhjMBl7a/hJpxWmMajHqpvdf\nbnk3dopSwlqpwMk2g5iSJPFJ709o49WGl7a/xO8pv9skDgD7li1xuftujOmphHf2IyU3gKxCN0j6\nw2YxCVD8+x8YCou4pGpGSDsvNA4qHol6hPec2/JQqR5a3W3rEP/h6krrMmMZbb3b8sPpHxi0ehCz\nj8ymSP+PUzwbBJHYa4BZNjOhgiOvAAAgAElEQVRjzwx2Z+zm313+TUe/jte9V5ZlCk9fIOlIFhH2\nv6GKvvkvgZrkqHbkizu/oJVnK84XnrdpLP7vvUvAf/5D2z6B2GlVxOnuh+M/2jSmxk4XF0dhSFcq\nKsChlaVbI8arLUPPH4DwAXX6+DsXjQtvdXuLtfespVdgL+Ydn8egVYNIKkiydWhWJxJ7DZh7dC7r\nz6/nX9H/YnT46Bveqzt4kL1P/QezGaKc/6gTx985a5xZPHAxk9tMBrBZq0bSXFl1m5VO645uXCiL\nIef4cdCL+cq24vfvVym5519IdmaePPswO9J2QPIOKM2y2djQrQpxDeHj3h+zYugKhoQOqdyH6Xj2\n8cpdU+s7kdhrQL+gfkxpO4XH2j5203sLli3Dr+wsvTy+xyOyNdi71EKEN3d1347zBecZvHowq86u\nskkcZp2OC6PvxffAEtQaSCppZzkTVah1V6dGu4RpOdRkMx0DYunm382yKMnOxXIYeT3S0qMlr3Z+\nFYWkQGfQMXXrVAatGsR3J7+r9wleJHYrkWWZPRl7AIj0jOSp9k/ddBDJmJ1N0ZatNOkeRhvNKmhz\n49a9Lfg7+dPaqzUz9s5g3bl1tV6/QqvF/YH7Kf9lPSPuc6dTk+2WU++FWiXLMsljxpLz9Tw22H3H\nkYCtvNHlDVQmo+XA6pZ3g7r+LhzTqrXMvmM2Ye5hfBT3Ub1P8CKxW4HJbOKdfe/w2JbH2JO+p8rP\nFaxaxUW/Phw0S5jVjnWyxeOgcmB239l0adKFGXtncDjzcK3H4DlpEko3N8oWzIGoEVSc2QXlhbUe\nR2NWfuIk5fHxnNLBL2c2MSFyAkEuQXBuK1QUQRvbjg1ZQ4xvDAvuWsDigYsrE/yZvDO2Duu2iMRe\nTQaTgek7p/Pj2R+Z3GZylXdJlGWZzA1bOR86DK1Jw3GnHjc8kMCW1Eo1H/f+mECnQJ79/VkySjJq\ntX6lszOejz9G6Z49nEqNZvGlr8jbu6lWY2jsCtetw+TgTPy55vTPup9H2z5qeeHkatB6Qkhv2wZo\nRR18O7DgrgWsHLqy8jSyzw9/Xq9a8CKxV0OZsYynf3+aX5N/5bkOz/FMzDNVnsMrSRLZ498ESUGM\ndiNf5rSr07sXutq5MqffHO4MvtMmZ6e6jxuHOjgIV8kAEsT9llfrMTRWsl5P0c8/U9xzLLJJ4pkx\nj1gW/OhL4cwv0GoY3OZOjXVZhEcEYJnllpCXUNlF833C9+hNehtHd2MisVfDocxD7MvYx4yuM3ik\n9SO39KzZLHNsfy526ougLGKnuU2d370w2CWYN7q+gZ3SrtbPTlXY2RG6YQP+jz5I2xaZJGaHkX+h\ndj85NFYlu3Zhys8n0bUZjm4amoRe2cnx7K9g0EHr+t8NcyMKScFXd37FogGLaO7WnA8OfsCwtcM4\nknXE1qFdl0jst+FqUusR0IMNIzYwKvzWfrD1qanse/AlVOVmumjX8IupEzqTst7sOZ5anMroDaPZ\nd2lfrdaruDL9MSLCGRV6Dq48VKv1N1Z2zZuTOrIbOXl2OIZKf34qPbEanPwguNuNC2ggYv1iWXDX\nAr6+82s87D3wdqi7B3yIxH6L0kvSGb1hdOUgaaBz4C2XUbB8ORWpGVQoCmhlv5sNZku/vEmW63yr\nHcDbwRuj2cgbu9+gRF9Sq3XrDh4k8/nXaMFezp13oLxUbDFQ08r83JgdXIydSUt0TJjlYnkhJG6B\nqOFQhw5Gr2mSJNEtoBs/DPnhtv7t1xaR2G/B+YLzTPhlApm6TLRq7W2VYdbrKVi1mixHmQjPL8lD\ny35zKwAMJrle7F5or7LnnR7vkKnL5OO4j2u1bofYWOyjovBN2MX9Hv/C3iz62mtS2dGjrF/yFima\nU3R9zofg1lfGV05vBFNFg++Gqa/EQRtVdCLnBFO3TkUpKVk0YFHlwMqtKt60iaIKOwa+MAnXvQ9A\n+wc4P2SYlaOtee282/Fw1MMsPLGQfkH96BlYO7suSpKE97RppE6ejDm5CE6th06P1krdjVHGgq8J\n3b+dwZ8MIyb8L/vgn1xtOdc08PrbZQi2I1rsVXCx6CKTNk3CUe3Ifwf997aTOkDu0uUcjXmWfUf0\nYCyDKNvs5GgN/4r+F2FuYaw4W7uHKjh274a2Y0eSk1uxcbWJopz6MQWtvpFNJir2x3E+wo8up0eR\nnVpseaEs33KuadTwWjnlS7h1IrFXQZBzEA+3fpjvBn1HU5emt12OLMuUdhtFhcqZFnY7LQNPQV2s\nGGnt0ig1fHnnl/ynz39qtV5JkvCY+AhKGS4UhJO4u+6PS9RH5SdPIhWXEN3rJdIOF2MymC0vnPoJ\nzMZ63Shp6ERiv4GNSRtJKUpBkiSmtpta7TNBJUkiWQ7B0VVNs/yFDWLgyc/RD5VCRWFFIfHZ8bVW\nr1OvXrRZNQdf9RkS96XWWr2NyZENi0CSyFL4YadV4RPsbHnh5BpwC7acbSrUSSKxX8eSU0t4eefL\nzI+fb5XyTCUlpHyznJSEPFq1KERhLoOoEVYpuy6YvnM6T/32FAXlBbVSn6RUovCPpIV3Irn59uRm\n1O7snIbuaNZRLuzZRHGID2nnSgls6Y5CqQBdnmVP/KgRohumDhOJ/X/Isszco3OZeWAm/YL68XqX\n161SbuH69SQs24UERCrXgksABDacQ3afjXmWQn0h7+5/t9bqNBUUoNp2FEk2k7gzsdbqbehkWWbW\noVksGO9Nk/fnU1pQQVDkldkwp9aDbLLZKV9C1YjE/hdm2czMAzP56thXDA8bzse9P67cvrY6ZFmm\nYOkywp0zGPN8S5zT1llaPIqG8+2P8Ihgarup/Jr8K78m1862uko3N7RuWgLzduJYerJW6mwMtqZs\n5XDWYR5v/wQKey/c/bQ0jfSwvHhyjeWgdb+2tg1SuKGGk1msoMJUwYncEzwU+RBvd3sblcI6s0HL\nDh+mIjER93H34VWyHUz6BtUNc9XE1hNp7dmad/e9S05ZTq3U6TpqLC3if6TFpa9qpb6GrtRQyswD\nM5ly2IPe6y8SEOHO/TO64OxhD6U5cGGH6IapB6yS2CVJGihJ0hlJks5JkjTdGmXWpnJjOTqDDgeV\nA9/c9Q3Pxz5v1QN5839Yyol2j3HOLtrS4nENgoAOViu/rlApVLzb411ifWNrrU6XoXeDUkHegXTy\nzojumOoq1hfTzKUZ/U5I6JOSMZvlP188tR5ks5gNUw9UO7FLkqQE5gKDgEhgnCRJkdUtt7aU6EuY\nunUqz/7+LLIsY6+yt2pSl81mCvP0ZLm3RZKNDX7+b6hbKLP6zsLLwQuzbK7x+lTu7jj36MJuzRNs\n+Pp85Sk/wu3xc/Tjy1ZvIqVnomvdh4Uv7OTyhSt7359cA55h4Btl2yCFm7JGi70TcE6W5SRZlvXA\nMuAeK5Rb4/LL85m0eRJHs44yPGy4VRP6VZJCQcHdTyMpoJXnUTAbGmQ3zP/KK8/j4V8f5o/UP2q8\nLs8nnqZFQBIlOg2ZFxrmqfM1zWA2MPvIbDJLMynZtQuAPJcw9GVG3H21UJIFybssrfUG2ihpSKyR\n2AOAv04kTrtyrU67XHqZh399mPMF5/nsjs8YHDrY6nXIJhMVWTmc2nuJkHbeOCavajTzf7UqLeXG\ncl7d+SoXCi/UaF0O7doReU8USvRidsxtkGWZGXtmMO/4PA5lHqJ05y7UTZuSccmMTzMX7LRqSFh3\npRum4TdKGoJaGzyVJGmKJElxkiTFZWdn11a11yTLMi9uf5FMXSZf3vklvQJ71Ug9JTt3snfsC5SX\nGGjdyQWSfm80A0/2Kns+7fspaqWayZsnk1KUUqP1SU2741dxnHNHcv7eLyzc1Owjs1l/fj1PRD/B\n4NDBqP39cRh8D1nJRTRtdXU2zFrwigCfVrYNVqgSayT2dOCv6+wDr1z7G1mW58myHCvLcqy3t233\nMZYkiTe7vsk3d31DR7+a28SoYOkyXJTFRPcLJNC8/coy7MbT4vF38mf+XfPRm/RM3DSR1OKaWyFq\n0tvhdu4YunINl8+L81CrasXZFcyPn8+oFqN4vO3jAPi9/hoVfccgyxDY0h2KL8PF3Y2mUdIQWCOx\nHwRaSJIUIkmSBrgPWG+Fcq0uuTCZecfnIcsyYe5hRHnV3CCQPi2dkh07CBzai+73hiMlrAH3EGjS\nrsbqrIvC3cNZcNcCvBy8UEg19wHRrlUr/O0z6HrufZq4185Uy/rOYDLw3cnv6BHQg9e6vIYkSRjz\n85FlGTcfLTEDgvELdYWE9YDcqBol9V21/6XJsmwEngQ2AaeAH2VZrnOrRS4UXuCRTY+w5NSSWplj\nXfDjj2T6dMDYYyiU5kLS9gY9G+ZGIjwiWDpkKQFOARjNRk7lnrJ6HZIk4TnyHhzS0tBvW2T18hsi\ntVLNkiFLmNlzZuWajZRHJpL+7DQ8/B3pOqI5SpXCMhvGJxJ8Wto4YqGqrNKEkmV5oyzL4bIsN5dl\nufbWlFfRhcILTNw0EVmWWThgId7amu0Kkk0mctes52yrB4g/rIPTGyzLsBvx/N+rM44WnVjE/Rvv\nZ9XZVVafDul673j0Gid+32TPpXO1s2dNfSTLMuvPr0dv0uOiccHVzhUAQ1YWFadPI0W0If1MPiaj\nGYoyIGWvaK3XMw1+5elfk/o3A76huVvzGq9TUioxTZ+DAQ1RvQIsZ0N6NAe/NjVed103tuVYOvh2\nYMbeGdyz9h6WnV6GzqCzStkqLy9c2wSQrIrlzI4zVimzIfrh9A/8e9e/WXd+3d+ul+62HPeY6xPN\n2llHKMjUWWbDIEPkcBtEKtyuBp/YkwqTUEiKWkvqV505WY6br5YAfwMk77RsmtQIu2H+l4vGha/u\n/Ir3e76Pk9qJd/e/y4w9M6xWfvAXcwnRxpF0rACzqeYXSNUnZtnMrEOzmHlgJr0DezMy7O+fIEt3\n7ULp7cWlAju0Lho8/B0t3TC+rcE73EZRC7ejwR+N1y+oH939u2Ovsq+V+srPnuX8rEVclvvTfXQY\n0un1Yv7v/1ApVNwdejdDQoZwLPtYZVdAekk66cXpdGpy+7teSq4BhAXmcO6cmozEAgJbelgr7Hqt\nzFjGqztfZWvKVu4Nv5dXOr+C8i9nAchGI6W7d+PYqzfpZ/JpGumBVJQOqfvhDuvscCrUngbfYgdq\nLakDFCxbRs7pdLTOKlp2afKX+b/1ZpeFWiNJEtE+0YS4hgDwfcL3PPnbkyTmV2+RkWN+OQpTBYnb\nrT9IW19dKLzAsexjvBj7Iq93eR21Qv2Pe5q8+w7ygHspKzbQtKWHpbUOolFSDzWKxF5bzKWlFK5b\nT4tOTXhoZg/s5dwry7DF/N+qeKT1IziqHXn6t6cprLj9uejO/UfQ5PI+pAtHrBhd/RbpGckvo35h\nQtSEa26dIalUOPfrR7bZMrEg8Gpib9IOPGuvC1OwDpHYrahww0+U6yVcx461nDYj5v/eEh+tD7P6\nzCJTl8mL21/EaDbeVjnaXncRlbma0MNfQSPfFCylKIX5x+djMpuwU9pd8x7ZaCTn63no09Jpe0cg\n974SixOXIP1Qo57JVZ+JxG4lsiyTv3Qp8R2fY/v+K0MXYv7vLYv2iebfnf/N3kt7b/tYQkmhwLVP\nB0pSjRQc2GXlCOsPg8nASzteYvHJxeSW5173Pt3Bg2TPmkV5wkmUSgU+wS6WLkQQjZJ6SiR2azEa\nMfYcSqHG17IMuzAdUvaIfxi3YVT4KF7u+DIjwm7/e+c64UnOho1l5eKiRjs75rPDn3Ey9yQzus24\n4UHsRb9uQtJqKQ1uz64VieiK9HByteXMAPfgWoxYsBaR2K1EUqtJb9IdlVpBRGc/SLjS4mk9yraB\n1VPjI8fj5+iHWTZjMBtu+Xm71rGEtsimQnYgI7HxLVbanrqdbxO+ZWzEWPoH97/ufbLRSPHmzTj3\n6UNyQhHxv6ehKrkIl46Jbph6TCR2KzDm55O7YROJBzIJ6+hr2eb0xCox8FRNBrOBKVum8Pnhz2/r\n+Vb3dUUllXN+Z4KVI6vbyo3lzNg7g5YeLXmx44s3vFd34ACm/HycBw0k7XQevqEuaM6Jbpj6TiR2\nKyhctYoj//kRQ4WJqJ7+kHfBMvAkWuvVolaoCXYO5tuT33Lw8sFbf771EPwNxzh3pKBRbeVrr7Ln\ns76f8XHvj687YHpVxYULKN3ckNp2Jiul2LJN74nVENQNXOv8sQrCdYjEXk2y2Uz+suU0DzAy5F9t\n8W3mIub/WtHzsc8T5BLE9B3TySvPu7WHtR54FyRSbtaScfr6g4cNhdFsZHf6bgDaercl2OXm/eMe\nDzxAix3bSTpRCDKEBRdDVoJlpbRQb4nEXk2lu3ZhSEvD8/6xNGvjZZkjfGI1BHYCtyBbh1fvadVa\nPu79MQUVBby669Vb3jisRe9A2pz4GpdzG2sowrrBLJt5c8+bPL71cRJyq9b1JJtMAEgaDUaDCf8W\nbrhnrgNJAZH14nRL4TpEYq+m/KXLuBA5hiTllZNlss9CZrxo8VjR1b7ipIIksnRZt/Ss+/gn8Sk4\nRunqJTUUne0ZzUbe2/9e5SlIkZ5VW+V8ecZbpEychCzLdBjYjOHToi1jQyG9wOn6s2iEuk8k9mow\nV1RQmppJim9P8nMqLBdPrgYksRuelY2NGMuae9bg5+h3S88p3H2wbx3I0awoLp+17ZGMNeFy6WUm\nbZrE8jPLeSTqkcpTkG5GNhgo3rIFpYcHFaVGZFlGunQU8i+IsaEGQCT2alDY2VExbRYmWUHrngGW\nVY7xKyG4O7g0sXV4DYokSTiqHTGZTby//32OZh2t8rOugwdyMbA/Jzfc+gBsXXc06yin804zs+dM\nnot97prbBVxL6f4DmAoKcBk4gPWfH2XT/JOW1rpCDS3vruGohZomEvttkg0GTDodCbsv4RPsjHeQ\ns2Xub24itL3X1uE1WMX6YnZn7OaJbU9wNv9slZ5xu+8JQp0OcSFZshweUc8lFSaxMckyZjAwZCA/\nj/yZIaFDbqmM4k2/otBqMUTEkp1SjF+Is2XQP6wfaMWOmPWdSOy3qXjLFuKGPExeRqnlMA2A+BWW\nFk+rYbYNrgFzs3fj6/5f46B04PEtj5NadPMDsiWNlhYRJioMdlzcf6EWoqwZJrOJL45+wah1o/g4\n7mMqTJbuPy8Hr1sqRzYYKN68Bac77iApPh+A5r6pUJQOrUdbPW6h9onEfpvyly5DrdXQvL03LWJ9\nwWyyfJRt0V+0eGpYgFMAX/f/GoPZwOTNk7lcevmmz/jHRKMylJKwcnctRGh92bpspmyZwpfHvmRg\nyEBWDF1x0znq1yObzXhPm4b7/eM4dyiLJs1dcb64AtRaaDnYypELtiAS+22oOHcO3cGDBI/ow8DH\n2qC2U8LFPVB8CdqIFk9tCHMP46v+X2EwG0gtvnmr3S5mCAElB9FnZCKb61d3TJG+iDE/jeF49nH+\nr/v/8X7P9/F08Lzt8hR2drjfNxZjUCty00sJbeth2fSr5RDQOFoxcsFWGvwJSjUhf+kyCj3C8e3z\nl37N+BWgdoTwQbYLrJGJ8oxi48iNlQepmMymv50K9DdKFT0iz3Fp6RF0u2Nx7Nm3FiOtHheNC1Pb\nTSXGJ4Yw97BqlWUuK6Nw3TpcBg3CwcWJAY+2xs8cB+UF0GaMlSIWbE2SbbBfdWxsrBwXF1fr9VqD\nuayMsz17sb/T6zg1C2DUSx3AWAEfh0P4ABg5z9YhNkorzq5gY9JGvrjzCxxUDte8x3xuN4kjJqLp\n0pmQ+d/VcoS3Rm/S89HBj+gf3L9aRwUaDAbS0tIoLy8HLD+/pvx8lJ6eKOyudOWU5lh+hl38xYEw\ndYS9vT2BgYGo1X8/6UqSpEOyLMfe7HnRYr9Fkr09mre/pGR9EZ17+Vsuntt2pcUjZsPYirPamUOZ\nh3hh+wt82vfTax79pmjejay2/Tgh3cPDuUU4eLrYINKbO5d/jpd3vszZ/LN4a72rldjT0tJwdnam\nWbNmSJJERXIysqMjmrAWlJUYsNcqUWZXgLYZuDW13psQbpssy+Tm5pKWlkZISMhtlSH62G+RJEkk\nZthjp1URFnNldd7x5aD1hNA+tgytURsYMpDXurzGjrQdvL779WtvPSBJhPYNwKxQc+Foeu0HeRNm\n2cz3Cd8z9qex5JTlMOeOOUxpO6VaZZaXl+Pp6YkkSZgNBswlJSjd3NCXmygtqMCkKwJkcHC3zpsQ\nqk2SJDw9PSs/Zd0OkdhvQdmxY1z499skHckmoosfKo0SygrgzC+WaWLKf7YShdozJmIMT7d/mp+T\nfuajgx9xrW5G/2GjcFFeInFX9Q7MrgnbUrbxwcEP6OLfhVXDVtG7aW+rlHt10ZK5wLIvvdLNjQqd\nEUkhoTbkgVIjBk3rmKouNLsekdhvQe7ixaTtTwIgqueVuesJa8FUAe3us2FkwlWT20zmwcgH8dH6\nXPvQZp+WNHc+Q1qGlrwjp2wQ4d8VVhRy4NIBAPoF9WNuv7nMuWPOLc9NrwpzRQUKBwckjQZ9uRE7\newWSvtjSWq+hvvXc3Fyio6OJjo7Gz8+PgICAyq/1en2VynjkkUc4c+bMDe+ZO3cuS5ZYZz+gHj16\nEBERQbt27ejRoweJiTduBFQlvtWrV3P69GmrxFclsizX+p8OHTrI9Y0+I0NOiIySL8/8QC4r0f/5\nwjcDZXl2rCybzbYLTvgb81/+X2Trsv/xetbKufKcx7bJO5+bXZth/U1+Wb782aHP5M5LOsvdfugm\nlxnKaqSehISEv31tNpnkijKDnJlcKJflZMty+mFZ1v+97szCMvner/bImUXWjenNN9+UP/roo39c\nN5vNsslksmpd1dG9e3f5yJEjsizL8ty5c+URI0ZUu8wHHnhAXrNmzS0987//72RZloE4uQo5VrTY\nqyj/h6WYUOL+wAPYO17pcslPtpxr2nasmE1Qh1xtqScVJjF0zVAWn1j8t9e9B46hQ+Y83Hb9gFzF\nVqO15Jfn8+mhTxmwagAL4hfQ3b87iwYuqpyyWVOuzt2XFApMBjOSJKEx5lgWJan/Xvfn2xI5mJzH\n59vO1Vg8586dIzIykgceeICoqCguXbrElClTiI2NJSoqirfffrvy3h49enD06FGMRiNubm5Mnz6d\ndu3a0bVrV7KyLLt9vvbaa3z66aeV90+fPp1OnToRERHBnj17ACgtLWXUqFFERkYyevRoYmNjOXr0\nxnsO9erVi3PnLN+HzZs3Ex0dTZs2bXj00UcrP3HcLL6dO3eyceNGpk2bRnR0NMnJycyaNYvIyEja\ntm3L+PHjrf79FYm9CsxlZeT/+COne73M77/k//nC8R8t/20r5v/WRU2dm9IjoAefHPqEr4599ecL\njl60ji5CWZjPW698SVbx7Q9S3aqU4hQWnlhIr8BerB62mk/6fEK4e3iN1imbzVQkJmLMyQHAwVmD\np68ShansH6uks4rKWXEoDVmGlXGpNfq9OX36NNOmTSMhIYGAgABmzpxJXFwcx44dY8uWLSQk/HNf\n+cLCQnr37s2xY8fo2rUrCxcuvGbZsixz4MABPvroo8pfErNnz8bPz4+EhARef/11jhw5ctMYN2zY\nQJs2bdDpdEycOJFVq1YRHx+PTqdj3rx/Tm2+Vnw9e/Zk8ODBzJo1i6NHj9KsWTM+/PBDjh49yvHj\nx5kzZ84tfuduTiT2KjCXl6McMIpM2Q93P63loizDsWXQrKc4UKOOUivUzOw5k2HNhzH36Fze2vsW\nBpPlYGzt8ElcDupKy7Pnrd4yLSgvYN25dXx78lsWxC/gzT1v8u6+dwFo592OTaM28VHvj6q92Kiq\nzKWlyAYDkkZTOaCsKM8DJLD/+2yYz7clYr5yj0mWa7TV3rx5c2Jj/5ySvXTpUmJiYoiJieHUqVPX\nTOwODg4MGmRZBNihQweSk5OvWfbIkSP/cc+uXbu47z7LWFi7du2Iioq6bmxjx44lOjqagwcP8uGH\nH3Lq1CnCw8Np3txyhvGECRPYsWPHbccXFRXF+PHjWbJkyT/mqluDmMdeBSp3dy63Ho7ij7Q/N/xK\nPwR556HHNNsGJ9yQUqHk/7r/H75aX+bHzyfYOZiHWz9MTtN+XA7KoRQXVh5M4el+Yfg43153iCzL\nZJRmEOBk+dl4fvvzHLh8oPJ1jULDqPBRlj3PJYkmTrW7pbOpoABJqUTh5ERxbjlms4ybOR/sXUD5\nZwq42lo3mCyJ3WCSWRmXWq3vzY04Ov45EycxMZHPPvuMAwcO4Obmxvjx46853U+j0VT+XalUYjQa\nr1m23ZXFVze650aWL19OdHR05deXL998P6JbiW/Tpk1s376d9evX895773H8+HGUyuusmr4NIrHf\nhO7wEQzlBk7tKaN5Bx8cXa+s1jvyPagcxBFi9YBCUvB0zNN08O1AJz/LYp/Xt/1KW88UKOxHE2Mh\nn287xzvDW1epvPzyfI5mHSU+J56TuSc5kXOCIn0Ru+7bhaudK0/HPI1GoaGpc1M0Sg1qhbra09du\nl2w2YyoqQuXugYxEhc6Inb0MZiM4/L0b5q+t9auuttqr+r25XUVFRTg7O+Pi4sKlS5fYtGkTAwcO\ntGod3bt358cff6Rnz57Ex8df8xPB9bRq1YrExESSkpIIDQ3l+++/p3fvqk9HdXZ2pri4GACTyURa\nWhp33HEHPXr0oGnTpuh0OpydnW/5PV2PSOw3IMsyme+/zwVTM/Q+A2nbN9Dygl5n2ckxaril1SPU\nC90DugOQWVjG7oKvOdyinIfi+jCw+BSL4uRrtkyL9EWczDnJydyTDAkZQhOnJmxN2crbe99GKSlp\n4d6C/sH9aePVBqVkaXG1825X6+/teuSyMrC3tyxKKrOclGQvFYGk/MfP7uGUgsrW+lUGk8zhi/nU\ntJiYGCIjI2nZsiXBwcF0797d6nU89dRTTJgwgcjIyMo/rq6uVXpWq9XyzTffMHLkSEwmE507d+bR\nRx+tct3jxo3jscce42Z8pr8AACAASURBVJNPPmH58uVMnDiR4uJizGYzL7zwglWTOoi9Yv6/vTsP\nq6raGzj+XcwoCKIIKg444cBwEEQcAzGHNM3xdU7Na8MtLdPsVlp6X2/d9GaD9ppTXsuQ0tTKechy\nHsAJpxBHBkVBRAQZzlnvH4cQYlQOHDisz/P0PJ2z9z77tw/yY+211/qtYqXuP8CNSZNwmj2X5Obd\naBXgom95nQqDDZNh/GZo2tXYYSqP6b0NZwg7cRZhf5wXr7jj+MCZbc3+hU2bF/hm+GvE3I9h0clF\nnL1zlqspV3OPWxi0kJ5NenIn/Q437t+gtVPrIuvSVBbnzpyhVf36mNepw73b6WRnaqljFo2oWRcc\n3IwdXoXKzs4mOzsbGxsboqKi6NWrF1FRUVhYVM727fnz52nTpk2+91StGAO4s+T/sHB1xXnoQFzy\n9J1x4huo3VS/BJ5S5URcTyYr0x4Sg9FZn8Lyng09T9Ql1FnfyLE2t+Zo/FE863rybPNn8azjSbu6\n7XCw1rfu6trWLZcJROVBWFhgUbcuWq2OzPRsathmI7KlvgRGNZOamkpISAjZ2fo7l6+++qrSJvWy\nKtNVCSHmA88CmUA0MEFKmWyIwIwt7dgx0o+Hc2PEx4gzybTwy6kLk3QFru6D4PfU2PUqasvUbrn/\nL+/7c3NkR+5dt2PSuIkAONdwZs/wPcYKz2Durg1D56EfSimEwK62DVZp13LGrlfuO43y4OjoSHh4\nuLHDqBBlHe64E/CUUnoDfwD/KHtIlUP27dtktAkk6mZN7t1Oe7Th5HeAAM1Io8WmGI6wd8ExSEOW\n1pLkn382djgGo01JIeHjj9GlpwNgZiaoYZOFhS61WrbWq5syJXYp5Q4p5Z/jeQ4DJtNpV+uZZ0h8\nbgYWVmaP6sLotPrE3rxHteufNGXJ7V9kf5ePuLbzmLFDMZjk779Hl5aGuZ0d2Zla0lMz0T1IAmGm\nKjlWA4acoDQR2GrAzzOKrIQE7v7wA6nJD/nj6C3adKr/qIRA9K+QEgO+hp8CrBiPc6cemJtnc7dR\nxUwYKm8yM5Ok1d9Qo1MgwtKS9NQs7ic+hPR7YOMIRa0ypZiMEvvYhRC7ANdCNr0rpdyUs8+7QDZQ\nZHk1IcRkYDJA48aVc6amlJKbc+fyYN9+kjJaotNJvEPyLD5wfCXUdIbW/Y0XpGJwFrbWNG+cwuXr\njegedwnLBlU7wd/bsoXshATqz/tfHkjJwwdZWFvrMNNlqW6YaqLEFruUsqeU0rOQ//5M6uOB/sBo\nWczYSSnlUimlv5TS39nZ2WAXYEj3t24ldddunKdMob5XQzQhjXCsl1NC4F4s/LEVfMeChVXxH6RU\nOa1CNGTKmhweN5fMIqaBVxXC3IKa3bpRs2tXsrN0SJ3EhmSwsK7wuuuGKNtbGrt27cLBwSH3s3v3\n7m2wzwaIiIhg27Ztua83bNjA/PnzDXoOQyrrqJg+wFvAU1LKtJL2r8wyr18nfvb72Ph44/T8OOpY\nWODuk+cPUMR/9fVh/J43XpBKuWno35oaqy8R76gh6b//xfX9940d0hNzeLY/Ds/q7yqzHmoxMwcr\n7V2wc6vwkVx16tTJraD4wQcfYGdnx/Tp0/Ptk1tq1qxsPcPBwcFs3LixTJ9RlIiICCIjI3Nnww4a\nNKhczmMoZe1jXwTYAzuFECeFEEtKOqAykjodsa+/AebmOM35mPAdMWQ+zFPjQZsF4f+FFj3149cV\nk2NmJujR3wqfjDCSf1yPNrnqjdrNvnuXpDVrkFotAFqtDqTExuIhwswMalSeh6Z/Ldt748YNHB0d\nc7evXbuWSZMmAXDr1i0GDx6Mv78/AQEBHD58uNTnGTNmTL5kb2dnB+hb+CEhIQwePBgPDw/GjRuX\nu8+RI0fo1KkTPj4+dOzYkQcPHjB37lzWrFmDRqNh3bp1LF++nNdffx2AK1euEBwcjLe3N08//TQx\nMTG55546dSqdO3emWbNmbNiw4cm/sMdUpha7lLJqd0bmEGZmOE+bBlJHeMRDzu2Po2UHF6xscr6e\nP7ZB6k3wX2jcQJVy1aRXTx7un8aVKB1Ja9bg/Pe/Gzukx5KwYAH3Nm6iZmAg1s2bY25uRo1altTU\nxeuT+vb34OYZw57U1Qv6fvREh164cIHVq1fj7+9fbKGuKVOm8NZbbxEYGMjVq1fp378/kZGRBfb7\n9ddfcwt3jRgxgrfffrvY80dERHD27FlcXFwIDAzk8OHDaDQaRowYwfr162nfvj337t3DxsaG2bNn\nExkZmVvzffny5bmf88orrzBp0iRGjx7N0qVLef3111m3bh0ACQkJHDhwgDNnzjB8+PAKa+mb5rSr\nx5CdlISFkxN2Xbtw73Ya50KP0LZbAxyc80zgOL4SajWElr2MF6hS/szMuePzKnEXzyGWL8dpzBjM\nS1lLxNjSjh/n3vofcXphItbNm5P5MBtttg4yHyCwhBqVb6bsX8v2FmXXrl35lp67e/cu6enp2Nrm\nn2T1uF0xgYGBNGjQACB3AQxra2saN25M+/btAUpVS+bIkSP88ssvgL6c76xZs3K3Pffccwgh8Pb2\nJja24hZQr9aJ/eHFi1wbOQrXOXOo1a8f+8KiMDMX+D/T9NFOt/+A6D0Q9E6+EqeKaYoz68qFOi3x\n7iKqTFKXmZnEf/ABlg0a5N5lnN0Xx9GfLtN1DGBZH6xqPHHLurzkLdtrZmaWb/HxvCV7/1w0I29J\n3NKysLBAl7N6lFarzXdn8GdpX3jy8r4lyXuOiqzLVW0X2tCmphI7ZSpmNWtSM7AjJ3Ze51pkIp0G\nN39UmhfgyBL9Ku7+E40XrFJhWnVrDphxLT4LHqagq+Cl855E4sqvybwUjcus9zCrUQNtto7Te27g\n4qrDTJcBNStfa/2vzMzMqF27NlFRUeh0unz90T179mTx4sW5r0tazi6vpk2b5pYR2LBhA9qc5w9F\nadu2LdevXyciIgLQlxPWarX5yu7+VWBgIN9/r19N7dtvv6V79+6ljq+8VMvELqUk/t33yIyJoeEn\n/8HC2Rl3n7r49WmCV1CeGaVpSXAqFLyGg13lHKKpGJZjvRo0bm7BqZRe3JzzJleHDkNmZRk7rGLV\n8GuP0wsTsQ8OBiDq+C1S72bga/+zvjxvFZlp+u9//5vevXvTuXNn3Nwe/R4uXryYAwcO4O3tTdu2\nbVm2bFmpP/PFF19k586d+Pj4cOLEiXwt6MJYW1sTGhrKyy+/jI+PD7169SIjI4MePXpw6tQpfH19\nc/vP88a3dOlSvL29CQsLY+FC4z+Lq5Zle5NWr+bWvz6k3vQ3sR8zHktr88IXQti/EHZ9AC8fBJei\nl9FSTMvNy/dY/3E47c034rh7J/VmzKDOC1Xjjk3qJGHzjiKzMhghnuXCc9tp4xto7LCUJ1CWsr3V\nssUOYN+7N7XGjmfTwhP89t3Fgjtos+DIUnB/SiX1asa1mQMerbOwrxGHXYc23F60iMycIWyVScq2\nbdz68EN0GRm57928kkJi3APaux5CWNqClZ0RI1SMpVomdqdx42iw8BP2fneRhGv3adyukGnW5zbB\n/TgIfKXiA1SMrueUnng2/ANXvxSEENycM7dCH36VRHv/PjfnzSPteDgiT03x+s0dGDGtBa3uLNBX\nIFV1YaqlapPYpVZL7LRp3N/zKwCn98Twx5FbBDzrTjPNX/rPpYRDi8GpuRriWF2ZmaPr8BKX42tg\nNWoIyQcPE3/mvLGjynV74adoE5NwnTMHYW6O1EkSrqUAUCfmW4QuEwKr1jh8xXCqTWK/s2QJKVu2\nkn37NjfOJXFw/SWa+Trj37dpwZ0v/wpxEdD5NSjjNGel6rpTbxC77r3OuSQtfwuZwZeXdcYOCYD0\n06e5GxpK7dGjsfXUdxOe3RfLDx8eJ/7CLTi2HFr1hbomMX9QeQLVImulHjjAnUWLcRg4EMfhwwBw\nbe5AyPNtEGaFPDT9fQHYNwDNqAqOVKlM6rWsT5P6d4i92556tumsO3ad2EPGrdkupeTWvH9h4eyM\n89QpAKTcSefAj9E0auuEa9J6SEuELlONGqdiXCY/4ybr5k3ips/AukULXGbPQghBo7ZOuLWpXfhI\nmKsH4NoB6PNvfTU8pVo706Q+DvEPGZp5j31xt0iZ8B0OS7/CzkhjlYUQ1P/oQ7ITbmNuZ4fUSfZ8\ncx4hIPh/miK+GQ5Nu0GTTkaJT6kcTL7Ffm/TT8iMDBp8+ik7v73MiZ3XAQpP6gD7FuhrrrcfV/h2\npdpISHnIqkupSJso0tK9ueNSnxv2LsTOeh9t6oMKj0eXMxvT2t2dmh0DAH0XTOzFZLoMaYH91TBI\nvQVPzazw2EqyceNGhBBcuHAh3/szZsygXbt2zJgxg40bN3Lu3Lkyn+vDDz+kRYsWeHh4sH379kL3\nWblyJV5eXnh7e+Pp6cmmTZsAWLVqFXFxcWWOwdhMPrHXmfw33Ddt5PQ5iD5xG7PCul7+FBuuLx/Q\n6e/6KdhKtfb57ih0UhJq5URti+sMNz/C537D0Sbc4vYn/6nQWKSUxE59ndi33sq/QQjcferStqOT\nft5F487QtGuFxlYaoaGhdO3aldDQ0HzvL126lNOnTzN//vwnSux/LQNw7tw51q5dy9mzZ9m2bRuv\nvPJKgdmmMTExzJs3j/3793P69GkOHz6Mt7c3oBJ7lSGE4MYda45tvkrrTq549yhmrdK9/9YvHeb/\nQsUFqFRaEdeTydJKYszseeCwi+E260ipbcM+zx7c/S6UtAqcZHd/+w5Sf/sNm9b5J6x4dm9I35e8\nEKfWwP14CJpZ4TXXS5Kamsr+/ftZsWIFa9euzX1/wIABpKam4ufnx5w5c/jpp5+YMWMGGo2G6Oho\noqOj6dOnD35+fnTr1i23tT9+/HheeuklOnbsyFt/+UO3adMmRowYgbW1Ne7u7rRo0YKjR4/m2ych\nIQF7e/vcEr52dna4u7uzbt06jh8/zujRo9FoNKSnpxMeHs5TTz2Fn58fvXv3Jj4+HoCgoCCmTp2K\nRqPB09Mz9xy//fZb7mIfvr6+RZYhKG8m38eeGJvKrlXnqde0Fk+N8ii6C+bqfojaDj3ngE2tig1S\nqZS2TO326MV9PzI/7cQSp3g83vs31ydORJuaWiFxaJOTuTVvHtZt2+A0biwAFw7HY2YmaNnBBZH9\nEPZ9Ao066ifUFWPCtgkF3uvdtDcjWo8gPTudV3YVnLcxsMVAnmvxHHcf3mXa3mn5tn3d5+sS49+0\naRN9+vShVatW1KlTh/DwcPz8/Pjpp5+ws7PLrf1y5coV+vfvz9ChQwEICQlhyZIltGzZkiNHjvDK\nK6+wZ88eQN/qPnjwIObm+cfpx8bGEhj4aKatm5tbgaqKPj4+uLi44O7unluT/dlnn2Xo0KEsWrSI\nBQsW4O/vT1ZWFq+99hqbNm3C2dmZsLAw3n33XVauXAlAWloaJ0+e5Pfff2fixIlERkayYMECFi9e\nTJcuXUhNTcXGxqbE76c8mHxiT7iWgk0NC555yQsLyyIma0gJO2frS/N2fLFiA1SqBntX/nB5h98i\nmlPj2Bmarl1bdCPBgB5e/IOY114j++5d3L5cjLCwIOVOOr+H/oGLey1adnCBw19CSiwMXlrpWuug\n74aZOlU/SmfEiBGEhobi5+dX7DGpqakcPHiQYcOG5b6XkWeG7bBhwwok9dIyNzdn27ZtHDt2jN27\nd/PGG28QHh7OBx98kG+/ixcvEhkZydNPPw3oq0PWr18/d/vIkSMB6N69OykpKSQnJ9OlSxemTZvG\n6NGjGTx4cL6aNxXJ5BN7m84NaOHngqV1Mf8Izm3U968PXAyWtkXvp1RrbUYN4+TprRzaZE7jbgEg\ntSSt/oYaAQG548kNzczWBmFlSZP/rsLWywspJb9+q++SCB7bGvHgDuxbCB79StW3XlwL29bCttjt\ntW1ql6qFnldSUhJ79uzhzJkzCCHQarUIIZg/f36xfxh1Oh2Ojo5FVnLMW/I3r4YNG3Ljxo3c1zEx\nMTRs2LDAfkIIAgICCAgI4Omnn2bChAkFEruUknbt2nHo0KFCz/XX+IUQvP322/Tr148tW7bQpUsX\ntm/fTuvWrYu8zvJi8n3sQPFJXZsFu+eCcxvwGVlxQSlVjrmdI4HddCSl1+XCpt3o0tJI+vpr4t99\nF2mg8r5SSu7v+ZX42e8jpcSqcWOa/fQTNXJauGf3xRFz4S6dh7TgoaVgx5dTkdnp8PQcg5zf0Nat\nW8fYsWO5du0aV69e5caNG7i7u7Nv374C++YtjVurVi3c3d354YcfAP33curUqRLPN2DAANauXUtG\nRgZXrlwhKiqKgICAfPvExcXlluUFfRngJk2aFIjBw8OD27dv5yb2rKwszp49m3tcWFgYAPv378fB\nwQEHBweio6Px8vJi5syZdOjQocAooIpSLRJ7sY6tgKTL0PMDVVdDKVHzwUNxsb3G0T2p6CytcJ3z\nARkXL3LnMUrJFiYzJpbElV9zZcgQYl55hQeHDqFNTAT0SzcCpKVkcmD9Jdxa16ZdtwaEbt5Bjwdb\nOew0EOq2LPO1lYfQ0NACy8ENGTKkwOgY0HfTzJ8/H19fX6Kjo1mzZg0rVqzAx8eHdu3a5Q5JLE67\ndu0YPnw4bdu2pU+fPixevLhAl01WVhbTp0+ndevWaDQawsLC+Oyzz4BHD2Y1Gg1arZZ169Yxc+ZM\nfHx80Gg0HDx4MPdzbGxs8PX15aWXXmLFihUAfPrpp3h6euLt7Y2lpSV9+/Z97O/MEKpl2d5cKXGw\nKAAadYAxP1bK/kml8onbvZVjm84T0s8au95/J/bN6aTs2IH7unXYeLR67M9L3rCR+H/8AwDrtm2o\nPWIEjoMGISwtC+wbHZFAvaa1SDeXXPxPL3xEFL21n7Fp5kDq2Rd8UFdY6Vel7IKCgnIfspaXspTt\nNfk+9mJtnQm6LOj3H5XUlVJrENKXgQnfwLE9ENAPl/fe5cGhQ8TPmkXTsNI/VJWZmQgrKxwGPIvM\nzKRm505YNWpU6L6Z6dlY2VrQvH09ALavmM9Qs9N8kDWOO9jz+e5L/O9znga7RqVqq75dMRe3wvmf\n4Km3wKmZsaNRqppn5nNfV4+LK5dg4ehIg48+xOXtmaVO6inbthPdrz9ZtxIQ5ubU/p/hRSb162cT\nWf3uQS6fvA3A7Zs3CLn+KeG6lqzW9iJLK1l3/AYJ9x8WerxieHv37i3X1npZVc/EnpEKW2boH5h2\nes3Y0ShVkUNDImp9wJ6oHiTv34Bd9+7UyFnZ/t7mzejS04s89P7evcROn46FszPCsuib5nu309jy\nf6f5+YtT2NhZ4uKun18Rv3YqNXnIzKy/ocv5FdZKyee7LxnwApWqrHom9i3T4V4MPPspWDz+yueK\nAuD/fF/MzHQc2nAJkvVD7DIuXyZuxltcGzO20FWX0o4dI3bq69h4eNBo6VdYODkV+tnh267y3Zwj\n3Lhwl06DmjNyVkf9IusXNuOdvJsvsp/jknw0RjpLK4m4drd8LlSpcqpfH/uJNfoFqoP+AY3VWpDK\nk6tZ2xa/kHoc2enP9RX/pPHri7Fu1gy3xYuImz6Dy/36U/elF3F64QXMrKzIunmTGy+9jGWDBjRa\nthRzu6KXrbO1t6JVgCuBA5pR0zGnymjSFdj4Mrh68eak/+NN1ShRilC9WuwJ52Hzm+DeHbrPMHY0\nignwHaDB0VHL75e7k719LgD2wcE02/wLdkFB3P7sc27N+xcAFnXqYN26NY1Xrii0pX54UzSnf9W3\n8tt2aUDIuDaPknpWOnyvLyfA8G/UnaZSrOqT2B/egx/Gg7U9DF6uxqwrBmFuaUb35/1o2DAb3eFl\ncP5nACxdXXH77FMaLVuGeS17AISlJU3XfItlnmnpfzq+9SrhW6+RGJdacG1VKWHzdLh5BgYvAyf3\ncr8uQ6uosr2JiYkEBwdjZ2fHq6++WuR+v/zyC76+vvj4+NC2bVu++uqr3DgNUTrY2KpHYs98AGuG\nQ2I0DFkO9i7GjkgxIY3aOBE8YwRWbm1g/SS48mhWpV23rtR7881ijz+1+wZHNl2mVUcXgkYWUqju\nwGdw8lvo/ha06l0el1DuKqpsr42NDf/85z9ZsGBBkcdkZWUxefJkfv75Z06dOsWJEycICgoCVGKv\nOrIzIGwMxByFIcugWfHV7xTliVjacK/3GjbcnUfKN3+HmNJNwDu15wb7f4iima8zIeMKWarxwOew\n633wHAJBb5dD4OWvIsv21qxZk65duxZbVfH+/ftkZ2dTp04dAKytrfHw8ODgwYOPHYO/vz+tWrXi\nl19+AeDs2bMEBASg0Wjw9vYmKirKoN9laZn2w1Nttr4FFb0HBiyCdoNKPkZRnlBadk0SdS1Yf2su\n/Ve8gfPET/SzmkvQzNeZXpPaYWb+l3bWwS9g5yxoNxgGLTVI9+G1sQVXBrPv2wenUaPQpadzY3LB\n6qYOgwbhOHgQ2XfvEjsl/1qqTb5ZXeI5K7Jsb2k4OTkxYMAAmjRpQkhICP3792fkyJF07tyZAQMG\nlDqGq1evcvToUaKjowkODubSpUssWbKEqVOnMnr0aDIzMwss8lFRTDux67L0D516fwjtxxo7GsXE\n1W/uwOAZHfj583B+vPkOPb6cS8tBA6DDJBLuZ/Bq6AkWjfKlnr0N95MeYu9kg0+PRngHu+XvfslI\nhW0z4cS3+sbI4GVgXnV/VStb2V6A5cuXc+bMGXbt2sWCBQvYuXMnq1ateqwYhg8fjpmZGS1btqRZ\ns2ZcuHCBTp06MW/ePGJiYhg8eDAtWxqnhk/V/ddSGpa2MCpMPShVKoxTg5oMfbsjW788wY7rb6Bd\n/zmtrx9ipW4sx66m8fmuKJ7R2XLm91j+550OZNqa5Uv4xJ3Q32UmRkO3NyHoHYMm9eJa2Ga2tsVu\nt6hdu1Qt9Lwqumzv4/Dy8sLLy4uxY8fi7u5eILGXFENhZXtHjRpFx44d2bx5M8888wxfffUVPXr0\nKHOsj8v0+9hVUlcqWE1HawbNDKDr0OY07xOEPLuRgeGfMCvrINl79Quqtw50pZazLZ/vjuL41Tts\n+3E1fDsElgbp7zKf/xlCZlfpljpUfNne0khNTWXv3r25r4sq21tSDD/88AM6nY7o6GguX76Mh4cH\nly9fplmzZkyZMoWBAwdy+vRpg8T8uEw/sSuKEZibm+HTswmWwa+z0OM7fk8bRnpqCI0y7Glvv57u\naVPJ+nY4Y06O4pTV3xh3ZQba+DP6FvrLB8C9W8knqQIqumwvQNOmTZk2bRqrVq3Czc2twCgXKSUf\nf/wxHh4eaDQa3n///dzW+uPE0LhxYwICAujbty9LlizBxsaG77//Hk9PTzQaDZGRkYwbV/CZRkUw\nSNleIcSbwALAWUp5p6T9K03ZXkUpZwkpD+n28a9kZOloobtPV7NztLY+y2C3eyQlJXEuzYHrurpE\n0BpHvyHMHaQx6PlV2d7yMX78+HwPWcuDUcv2CiEaAb2A62X9LEUxNZ/vjkInJQi4ZG7PJTpiqQ3k\nqGMDNl+PJyNbl7uvTXg8r/ZsXWhddUV5HIboilkIvAVU/IodilLJRVxPJkub/1cjSyv59UKCPuHn\noSo0Vh2rVq0q19Z6WZWpxS6EGAjESilPVcSK7YpS1WyZWnhf+TOf7eNcfEq+91SFRsVQSkzsQohd\ngGshm94F3kHfDVMiIcRkYDLoHzooSnVWVMJXFEMoMbFLKXsW9r4QwgtwB/5srbsBEUKIACnlzUI+\nZymwFPQPT8sStKIoilK0J+6KkVKeAer9+VoIcRXwL82oGEVRFKX8qHHsiqKUu8pQtjc8PBwvLy9a\ntGjBlClTCpZHBi5evEhQUBAajYY2bdowefJkQD+JacuWLWWKrSIZLLFLKZuq1rqiKIWpDGV7X375\nZZYtW0ZUVBRRUVFs27atwD5TpkzhjTfe4OTJk5w/f57XXtOviVxtE7uiKEphKkPZ3vj4eFJSUggM\nDEQIwbhx49i4cWOBWOPj43Fze7SWrJeXF5mZmcyePZuwsDA0Gg1hYWE8ePCAiRMnEhAQgK+vb+6M\n1FWrVjFw4ECCgoJo2bIlc+bMAeDBgwf069cPHx8fPD09CQsLM8yXW4SqXYhCUZTHsuE/EQXea+FX\nD68gN7IytfzyRcF6LK071adN5/qkp2ay7avIfNsGvdm+xHNWhrK9sbGx+RK2m5sbsbGxBfZ74403\n6NGjB507d6ZXr15MmDABR0dH5s6dy/Hjx1m0aBEA77zzDj169GDlypUkJycTEBBAz576cSZHjx4l\nMjKSGjVq0KFDB/r168e1a9do0KABmzdvBuDevXulivtJqRa7oijlKjQ0lBEjRgCPyvaWJG/JXI1G\nw4svvkh8fHzu9rKW7S3KhAkTOH/+PMOGDWPv3r0EBgbmK9X7px07dvDRRx+h0WgICgri4cOHXL+u\nn3z/9NNPU6dOHWxtbRk8eDD79+/Hy8uLnTt3MnPmTPbt24eDg4PBY89LtdgVpRoproVtaWVe7HZb\nO6tStdDzqixlexs2bEhMTEzu65iYGBo2bFjovg0aNGDixIlMnDgRT09PIiMjC+wjpWT9+vV4eHjk\ne//IkSOFlvNt1aoVERERbNmyhffee4+QkBBmz579WNfwOFSLXVGUclNZyvbWr1+fWrVqcfjwYaSU\nrF69moEDBxbYb9u2bWRlZQFw8+ZNEhMTadiwYb7YAHr37s0XX3yRO7LmxIkTudt27txJUlIS6enp\nbNy4kS5duhAXF0eNGjUYM2YMM2bMICKiYJeYIanErihKualMZXu//PJLJk2aRIsWLWjevDl9+/Yt\ncOyOHTvw9PTEx8eH3r17M3/+fFxdXQkODubcuXO5D09nzZpFVlYW3t7etGvXjlmzZuV+RkBAAEOG\nDMHb25shQ4bg7+/PmTNnctdCnTNnDu+9997jfI2PzSBlex+XKturKBVDle2tWKtWrcr3kLUsylK2\nV7XYFUVRTIx68VLdjAAABGZJREFUeKooimIg48ePZ/z48cYOQ7XYFUVRTI1K7Ipi4ozxHE0pm7L+\nzFRiVxQTZmNjQ2JiokruVYiUksTExAJlER6H6mNXFBPm5uZGTEwMt2/fNnYoymOwsbHJVwLhcanE\nrigmzNLSEnd3d2OHoVQw1RWjKIpiYlRiVxRFMTEqsSuKopgYo5QUEELcBy5W+IkrTl3AlFeTMuXr\nM+VrA3V9VZ2HlNK+pJ2M9fD0YmnqHVRVQojj6vqqJlO+NlDXV9UJIUpVZEt1xSiKopgYldgVRVFM\njLES+1IjnbeiqOurukz52kBdX1VXquszysNTRVEUpfyorhhFURQTY9TELoR4TQhxQQhxVgjxsTFj\nKS9CiDeFEFIIUdfYsRiKEGJ+zs/ttBBigxDC0dgxGYIQoo8Q4qIQ4pIQ4m1jx2NIQohGQohfhRDn\ncn7fpho7JkMTQpgLIU4IIX4xdiyGJoRwFEKsy/m9Oy+E6FTc/kZL7EKIYGAg4COlbAcsMFYs5UUI\n0QjoBVw3diwGthPwlFJ6A38A/zByPGUmhDAHFgN9gbbASCFEW+NGZVDZwJtSyrZAIPB3E7s+gKnA\neWMHUU4+A7ZJKVsDPpRwncZssb8MfCSlzACQUiYYMZbyshB4CzCpBxlSyh1Syuycl4eBJy9DV3kE\nAJeklJellJnAWvQND5MgpYyXUkbk/P999ImhoXGjMhwhhBvQD1hu7FgMTQjhAHQHVgBIKTOllMnF\nHWPMxN4K6CaEOCKE+E0I0cGIsRicEGIgECulPGXsWMrZRGCrsYMwgIbAjTyvYzChxJeXEKIp4Asc\nMW4kBvUp+kaUztiBlAN34DbwdU5X03IhRM3iDijXmadCiF2AayGb3s05txP628IOwPdCiGayCg3T\nKeH63kHfDVMlFXdtUspNOfu8i/4Wf01FxqY8OSGEHbAeeF1KmWLseAxBCNEfSJBShgshgowdTzmw\nANoDr0kpjwghPgPeBmYVd0C5kVL2LGqbEOJl4MecRH5UCKFDX+ehyqwIUNT1CSG80P+VPSWEAH1X\nRYQQIkBKebMCQ3xixf3sAIQQ44H+QEhV+mNcjFigUZ7XbjnvmQwhhCX6pL5GSvmjseMxoC7AACHE\nM4ANUEsI8a2UcoyR4zKUGCBGSvnnHdY69Im9SMbsitkIBAMIIVoBVphI8R4p5RkpZT0pZVMpZVP0\nP5j2VSWpl0QI0Qf9be8AKWWaseMxkGNASyGEuxDCChgB/GTkmAxG6FsYK4DzUspPjB2PIUkp/yGl\ndMv5XRsB7DGhpE5O3rghhPDIeSsEOFfcMcZcQWklsFIIEQlkAs+bSMuvOlgEWAM7c+5IDkspXzJu\nSGUjpcwWQrwKbAfMgZVSyrNGDsuQugBjgTNCiJM5770jpdxixJiU0nsNWJPT6LgMTChuZzXzVFEU\nxcSomaeKoigmRiV2RVEUE6MSu6IoiolRiV1RFMXEqMSuKIpiYlRiVxRFMTEqsSuKopgYldgVRVFM\nzP8DLjeoqma19w4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "JNQ1wwWBB2ah",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### MAML vs Regular Neural Network"
      ]
    },
    {
      "metadata": {
        "id": "sdJ1JzsnB2ai",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def compare_maml_and_neural_net(maml, neural_net, sinusoid_generator, num_steps=list(range(10)),\n",
        "                                intermediate_plot=True, marker='x', linestyle='--'):\n",
        "    '''Compare the loss of a MAML model and a neural net.\n",
        "    \n",
        "    Fits the models for a new task (new sine wave) and then plot\n",
        "    the loss of both models along `num_steps` interactions.\n",
        "    \n",
        "    Args:\n",
        "        maml: An already trained MAML.\n",
        "        neural_net: An already trained neural net.\n",
        "        num_steps: Number of steps to be logged.\n",
        "        intermediate_plot: If True plots intermediate plots from\n",
        "            `eval_sinewave_for_test`.\n",
        "        marker: Marker used for plotting.\n",
        "        linestyle: Line style used for plotting.\n",
        "    '''\n",
        "    if intermediate_plot:\n",
        "        print('MAML')\n",
        "    fit_maml = eval_sinewave_for_test(maml, sinusoid_generator, plot=intermediate_plot)\n",
        "    if intermediate_plot:\n",
        "        print('Neural Net')\n",
        "    fit_neural_net = eval_sinewave_for_test(neural_net, sinusoid_generator, plot=intermediate_plot)\n",
        "    \n",
        "    fit_res = {'MAML': fit_maml, 'Neural Net': fit_neural_net}\n",
        "    \n",
        "    legend = []\n",
        "    for name in fit_res:\n",
        "        x = []\n",
        "        y = []\n",
        "        for n, _, loss in fit_res[name]:\n",
        "            x.append(n)\n",
        "            y.append(loss)\n",
        "        plt.plot(x, y, marker=marker, linestyle=linestyle)\n",
        "        plt.xticks(num_steps)\n",
        "        legend.append(name)\n",
        "    plt.legend(legend)\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "scrolled": false,
        "id": "Z_dSYXNEB2ak",
        "colab_type": "code",
        "outputId": "549c16d3-cc0d-4c62-8406-7f5090424cc7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2397
        }
      },
      "cell_type": "code",
      "source": [
        "for _ in range(3):\n",
        "    index = np.random.choice(range(len(test_ds)))\n",
        "    compare_maml_and_neural_net(maml, neural_net, test_ds[index])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MAML\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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HjcL5uXnkrl6NeVgYIJW9Lr9wHptJkxCUSrr0d8PZ2xo7VwsUCoF3wt/lhT4v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\noihKI6MSu6IoSiPz/wGBZFpbwLyzeAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Neural Net\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7bPRG/e1fqNZAs14N4rPbsI09NRJ0JTUKw4iiyIa3N3Ip2UDrogPc/+koXLuH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\nRalnVGJXFEWpZ1RiVxRFqWf+D1D2x34JBO6hAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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aLHMEDc2tLN9Uxb79zcEORSmluk2T+xF8V17PvCdXsGLL3mCHopRS3abJ/QhG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\nYzC+vgf9lAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "MAML\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fOtUscXjaefLj0B9p7d6at/a/xbXCa2ZpF8Bw7So5v/6GU04CYb18iT+RjlFf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\niqLUM/8fjD8IAaLfEu0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Neural Net\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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t0VuYc3gOH5z+AI1OU+32jJGS2GuJSqPilSOv8L/w//FE+ydY9sCyf5UGuE2o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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rrA51KUII4TdRHdxgxnOXbzuI1jrUpQghhF/EQHCns6e2ia9q5EIcIUR0iPrg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\nhJHgFkKICPP/ASnv2Q0jAVGIAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "MAML\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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sGrCIKM8o/r3r31wuvVyj9d2MtnNnQtasxr5ZMB0GNiM/rZSfK0bRRpFMiJzC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\nzP8DLjeoqma19w4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Neural Net\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MoRiFcuT8elBZy8OqTUQIgSElBc2NKMjJRuh0bD9gS9ot3V1ytdzh0UFqUFuxYw+kJObf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\n7IjIfowAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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m424vKyU+0CEIIYRPhc0l7+0tKqrgzTXlgQ5DCCF8IiwT99vrKvjLkm2BDkMI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\njCRuIYQIMf8fbpwAjczQGs4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}
